Neurobiologically-Based Subtyping of Multi-Cohort Samples with MDD and PTSD Symptoms

具有 MDD 和 PTSD 症状的多队列样本的基于神经生物学的亚型

• 批准号: 10609903
• 负责人: RAJENDRA A MOREY
• 金额: $ 55.95万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609903
• 关键词: Age; Algorithms; Arousal; Attention; Behavior; Biological Testing; Brain; Brain region; Categories; Characteristics; Chronic; Classification; Clinical; Collaborations; Control Groups; Coupled; Data; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease model; Dorsal; Exposure to; Fright; Functional Magnetic Resonance Imaging; Future; Gender; Goals; Heterogeneity; Hybrids; Individual; Infrastructure; Life Stress; Major Depressive Disorder; Maps; Medical; Mental disorders; Modeling; Modernization; Neurobiology; Participant; Patients; Pattern; Population; Post-Traumatic Stress Disorders; Recording of previous events; Research; Research Domain Criteria; Resources; Rest; Sampling; Severity of illness; Sleep disturbances; Stress; Stressful Event; Symptoms; Syndrome; Technology; Testing; Time; Trauma; algorithm training; biobank; biotypes; brain circuitry; brain dysfunction; cohort; comorbidity; depressive symptoms; disease classification; effective therapy; experience; interest; large datasets; learning strategy; neural; neural circuit; neurobiological mechanism; neuropsychiatric symptom; novel; patient subsets; pediatric trauma; precision medicine; psychiatric comorbidity; psychological trauma; reward processing; supervised learning; symptom cluster; therapy design; translational neuroscience; trauma exposure; unsupervised learning

ABSTRACT Significant symptom overlap and high rates of co-occurrence between syndromes of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) call into question whether the two are distinct disorders. The onset and course of both syndromes are strongly influenced by environmental variables. We hypothesize that a continuum of life stress or adversity and an independent continuum of psychological trauma conspire to influence the onset of PTSD and MDD (where at least one trauma exposure is required for PTSD). Our overarching goal is to identify and compare neural signatures of MDD, PTSD, symptom features common to PTSD and MDD, and heretofore unrecognized neurobiologically-defined syndromes. Therefore, we plan to investigate neural signatures with supervised learning, and to identify biotypes that cut across disorders (PTSD and MDD) with unsupervised learning, an approach that can better explain contributions of trauma, stressful life events, and disease characteristics than possible with DSM-disorders. Rather than subtyping patients on the basis of clinical symptoms or DSM-defined diagnoses, our goal is to identify distinct clusters of neurobiological subtypes with disrupted neural signatures derived from resting-state fMRI. In Aim 1 we propose to train algorithms with supervised learning to detect neural signatures from resting fMRI data that can classify DSM diagnosis of comorbid PTSD and MDD, PTSD only, MDD only, and Controls (no psychiatric disorder). The analysis will be performed separately with MDD and Control groups who experienced criterion-A trauma or stressful life events, and those who did not. In Aim 2, we plan to use supervised learning in MDD and PTSD patients to identify neural signatures from resting-state fMRI data associated with four trans-diagnostic symptoms that include disrupted sleep, irritability, concentration difficulties, and loss of interest. In Aim 3, we propose to apply unsupervised learning methods to identify novel biotypes associated with specific symptoms or symptom clusters. The algorithms will employ rsfMRI features in patients with (1) PTSD only, (2) MDD only and (2) across PTSD, MDD, and comorbid PTSD+MDD patients in order to identify potential trans-diagnostic biotypes that cut across DSM boundaries. We will investigate associations of diagnosis-specific and trans- diagnostic biotypes derived from unsupervised learning with stressful life events, trauma exposure, developmental stage at time of exposure, psychiatric comorbidities, medical comorbidities, illness chronicity, illness severity, gender, and age. The overlapping and intersecting patterns that maps circuit disruption to psychiatric syndromes presents a daunting challenge in designing treatments that intervene at the circuit level. Developing a neurobiologically-based nosology that maps to clinical symptoms and syndromes represents a major advance in translational neuroscience. The advent of modern brain stimulation technology offers an unprecedented possibility of intervening at the circuit level with precision medicine strategies.

抽象的 创伤后应激综合症之间明显的症状重叠和高共现率 精神障碍（PTSD）和重度抑郁症（MDD）让人质疑这两者是否是不同的疾病。 这两种综合征的发作和病程都受到环境变量的强烈影响。我们假设 生活压力或逆境的连续性和心理创伤的独立连续性共同导致 影响 PTSD 和 MDD 的发作（其中 PTSD 至少需要一次创伤暴露）。我们的 总体目标是识别和比较 MDD、PTSD 的神经特征、常见症状特征 创伤后应激障碍（PTSD）和抑郁症（MDD），以及迄今为止未被认识的神经生物学定义的综合症。因此，我们计划 通过监督学习研究神经特征，并识别跨疾病的生物型（PTSD 和MDD）与无监督学习，一种可以更好地解释创伤、压力的贡献的方法 生活事件和疾病特征可能比 DSM 障碍更明显。而不是对患者进行亚型分类 根据临床症状或 DSM 定义的诊断，我们的目标是识别不同的集群 神经生物学亚型，其神经特征源自静息态功能磁共振成像 (fMRI)。在目标 1 中，我们建议 通过监督学习训练算法，从可分类的静息功能磁共振成像数据中检测神经特征 DSM 诊断共病 PTSD 和 MDD、仅 PTSD、仅 MDD 和对照（无精神疾病）。 该分析将分别针对经历过 A 级创伤或经历过 MDD 的对照组和对照组进行。 那些有压力的生活事件，以及那些没有压力的生活事件。在目标 2 中，我们计划在 MDD 和 PTSD 中使用监督学习 患者从与四种跨诊断相关的静息态功能磁共振成像数据中识别神经特征 症状包括睡眠中断、烦躁、注意力不集中和失去兴趣。在目标 3 中，我们 提议应用无监督学习方法来识别与特定症状相关的新生物型 或症状群。该算法将在 (1) 仅患有 PTSD、(2) 仅患有 MDD 的患者中采用 rsfMRI 特征 (2) 跨 PTSD、MDD 和共病 PTSD+MDD 患者，以确定潜在的跨诊断 跨越 DSM 界限的生物型。我们将调查诊断特异性和反式诊断之间的关联 诊断生物型源自无监督学习，包括压力生活事件、创伤暴露、 暴露时的发育阶段、精神合并症、医学合并症、慢性疾病、 疾病严重程度、性别和年龄。将电路中断映射到的重叠和交叉模式 精神综合征对设计电路层面的干预治疗提出了严峻的挑战。 开发一种基于神经生物学的疾病分类学来映射临床症状和综合征代表了 转化神经科学的重大进展。现代脑刺激技术的出现提供了 通过精准医学策略在电路层面进行干预的可能性前所未有。